Skip to main content

PPM is Evolving with Strategic Project Management on the Rise

Jordy Dekker
ValueBlue

Enterprises have had access to various Project and Portfolio Management (PPM) tools for quite a few years, to guide in their project selection and execution lifecycle. Yet, in spite of the digital evolution of management software, many organizations still fail to construct an effective PPM plan or utilize cutting-edge management tools.

Digital innovation and disruptive technologies are creating change at warp speed. Organizations are faced with the enormous task of trying to stay on track, while on the fast track. The project design ecosystem in many businesses is too big, too siloed, too complex, and too costly. There is increasing pressure to pivot quickly in response to ever-changing demands, while still retaining control of day-to-day tasks and delivering on strategic initiatives.

Project Management Challenges that Impede Success

The most common factors that contribute to project failures emanate from a position of deficiency or lack of:

■ Resource planning leading to project overruns

■ Visibility for all stakeholders

■ Alignment with core business strategies and initiatives

■ Cohesive communication across the enterprise

■ Resistance to new methods and technologies

■ Collaboration across project teams with multiple silos

■ Realistic delivery expectations with schedule delays

■ Effective prioritization with too many projects at one time

Business analysts estimate that at least 50% of projects fail for these reasons, and many place that figure much higher. Without a clear PPM plan, executed with advanced intuitive and automated tools, an enterprise will continue to flounder and experience poor performance and financial waste.

PPM Technology Innovations Change the Corporate Culture

Effective project and portfolio management enables an enterprise to make the best decisions to successfully execute its strategic goals. The clarity and visibility attained with the right management software tools can empower stakeholders to stay in control of project design, intent, resources and timelines, while still managing day-to-day business needs.

Innovative PPM technology fosters collaboration and communication between departments and business units for a holistic approach. From the C-suite down, a shared vision and transparency breaks down silos and allows all stakeholders to visualize the bigger picture. One of the impediments to successful engagement of digital innovations has been the resistance of siloed departments to change habits and methods. Through training and strong leadership, the advantages of a collaborative culture through adoption of digital innovations can reveal greater efficiencies across the enterprise.

Advanced PPM platforms that integrate business strategy capabilities can consolidate all project-related data into a single centralized repository that is equipped with advanced analytics for effective data sharing with all teams and stakeholders. Through streamlining and automating more basic daily tasks and processes, team members are able to conserve time, increase productivity and focus on core project elements.

Projects often influence each other and can create interdependencies for resources and deliverables. With the use of more advanced, integrated and intuitive dashboards, that provide real-time data and key metrics, project viability can be assessed and decision-making clarified as to what projects to hold, begin or eliminate. The ability to estimate the potential ROI of a project can significantly and positively impact the bottom line.

PPM Is Being Redefined as Strategic Portfolio Management (SPM)

Project management, as part of the project portfolio architecture, needs strategy, flexibility, and agility. By layering the business strategy over the execution layer of projects and programs, there is continuous monitoring of initiatives, investments, and delivery. The concept of SPM is quickly replacing PPM to describe a successful management process.

So, what is the difference?

Business transformation in the form of projects should not be disconnected from strategy. SPM is an integrated software approach that enables an enterprise to align all project execution with strategy and more effectively drive business agility. SPM integrates planning, funding and overseeing of all discretionary investments in the management of the project portfolio architecture. It utilizes data-fueled metrics and continuous monitoring to constantly evaluate investments against results. This cohesive approach empowers greater insights to reprioritize, adjust, or cut projects during the lifecycle for continuous portfolio optimization.

As the modern permutation of PPM, Strategic Portfolio Management facilitates the right projects, done the right way, at the right time. According to Gartner, over 50% of PPM leaders plan to integrate SPM technologies to help them define projects and strategic goals. These statistics highlight the importance of project and portfolio management technology innovations.

■ 77% of high-performing projects use project management software.

■ Organizations that use proven PPM practices waste 28 times less money than their counterparts who do not have PPM practices in place.

■ 8 out of 10 project managers believe that project portfolio management is becoming a critical factor in influencing business success.

■ 67% of projects of organizations that undervalue project management result in failure.

■ Failed IT projects have cost U.S.-based enterprises $50–$150 billion in lost revenue and productivity.

Jordy Dekker is Chief Evangelist at ValueBlue

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

PPM is Evolving with Strategic Project Management on the Rise

Jordy Dekker
ValueBlue

Enterprises have had access to various Project and Portfolio Management (PPM) tools for quite a few years, to guide in their project selection and execution lifecycle. Yet, in spite of the digital evolution of management software, many organizations still fail to construct an effective PPM plan or utilize cutting-edge management tools.

Digital innovation and disruptive technologies are creating change at warp speed. Organizations are faced with the enormous task of trying to stay on track, while on the fast track. The project design ecosystem in many businesses is too big, too siloed, too complex, and too costly. There is increasing pressure to pivot quickly in response to ever-changing demands, while still retaining control of day-to-day tasks and delivering on strategic initiatives.

Project Management Challenges that Impede Success

The most common factors that contribute to project failures emanate from a position of deficiency or lack of:

■ Resource planning leading to project overruns

■ Visibility for all stakeholders

■ Alignment with core business strategies and initiatives

■ Cohesive communication across the enterprise

■ Resistance to new methods and technologies

■ Collaboration across project teams with multiple silos

■ Realistic delivery expectations with schedule delays

■ Effective prioritization with too many projects at one time

Business analysts estimate that at least 50% of projects fail for these reasons, and many place that figure much higher. Without a clear PPM plan, executed with advanced intuitive and automated tools, an enterprise will continue to flounder and experience poor performance and financial waste.

PPM Technology Innovations Change the Corporate Culture

Effective project and portfolio management enables an enterprise to make the best decisions to successfully execute its strategic goals. The clarity and visibility attained with the right management software tools can empower stakeholders to stay in control of project design, intent, resources and timelines, while still managing day-to-day business needs.

Innovative PPM technology fosters collaboration and communication between departments and business units for a holistic approach. From the C-suite down, a shared vision and transparency breaks down silos and allows all stakeholders to visualize the bigger picture. One of the impediments to successful engagement of digital innovations has been the resistance of siloed departments to change habits and methods. Through training and strong leadership, the advantages of a collaborative culture through adoption of digital innovations can reveal greater efficiencies across the enterprise.

Advanced PPM platforms that integrate business strategy capabilities can consolidate all project-related data into a single centralized repository that is equipped with advanced analytics for effective data sharing with all teams and stakeholders. Through streamlining and automating more basic daily tasks and processes, team members are able to conserve time, increase productivity and focus on core project elements.

Projects often influence each other and can create interdependencies for resources and deliverables. With the use of more advanced, integrated and intuitive dashboards, that provide real-time data and key metrics, project viability can be assessed and decision-making clarified as to what projects to hold, begin or eliminate. The ability to estimate the potential ROI of a project can significantly and positively impact the bottom line.

PPM Is Being Redefined as Strategic Portfolio Management (SPM)

Project management, as part of the project portfolio architecture, needs strategy, flexibility, and agility. By layering the business strategy over the execution layer of projects and programs, there is continuous monitoring of initiatives, investments, and delivery. The concept of SPM is quickly replacing PPM to describe a successful management process.

So, what is the difference?

Business transformation in the form of projects should not be disconnected from strategy. SPM is an integrated software approach that enables an enterprise to align all project execution with strategy and more effectively drive business agility. SPM integrates planning, funding and overseeing of all discretionary investments in the management of the project portfolio architecture. It utilizes data-fueled metrics and continuous monitoring to constantly evaluate investments against results. This cohesive approach empowers greater insights to reprioritize, adjust, or cut projects during the lifecycle for continuous portfolio optimization.

As the modern permutation of PPM, Strategic Portfolio Management facilitates the right projects, done the right way, at the right time. According to Gartner, over 50% of PPM leaders plan to integrate SPM technologies to help them define projects and strategic goals. These statistics highlight the importance of project and portfolio management technology innovations.

■ 77% of high-performing projects use project management software.

■ Organizations that use proven PPM practices waste 28 times less money than their counterparts who do not have PPM practices in place.

■ 8 out of 10 project managers believe that project portfolio management is becoming a critical factor in influencing business success.

■ 67% of projects of organizations that undervalue project management result in failure.

■ Failed IT projects have cost U.S.-based enterprises $50–$150 billion in lost revenue and productivity.

Jordy Dekker is Chief Evangelist at ValueBlue

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.